RADARSAT-2 polarimetric SAR data for urban land cover mapping using spatial- temporal SEM algorithm and mixture models
Bibliographic record
Abstract
This paper presents a multi-temporal Stochastic Expectation-Maximization (SEM) algorithm with adaptive Markov Random Field (MRF) for analysis of polarimetric SAR (PolSAR) data for urban land cover mapping. The fitness of alternative distributions of multi-look PolSAR data based on Wishart, G0pand Kpassumptions are compared by using the SEM algorithm. The proposed pixel-based SEM algorithm explores the spatial-temporal contextual information to improve the classification accuracy while simultaneously overcomes the pepper-salt effect of speckle. Owing to the adaptive MRF analysis, the iterative process of the supervised SEM algorithm becomes stable. Further, detailed shape features could be preserved comparing with the traditional MRF methods. Four-date RADARSAT-2 polarimetric SAR data over the Greater Toronto Area are used for the experiment. The results show that this algorithm could generate reasonable classification accuracy for detailed urban land cover mapping. And the fitness of G0pand Kpdistributions are proven to be better for urban land cover mapping than that of Wishart distributions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".